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Volatility Clustering in Option Pricing Problems

Student: Khachatryan Van

Supervisor: Alexey S. Shvedov

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Year of Graduation: 2016

Over the past couple of decades, the study of the statistical characteristics of financial time series revealed many interesting facts that are quite common regardless of the market or financial environment. In this paper, we will focus on the phenomenon of volatility clustering, which often can be observed in the financial markets. This paper is dedicated to futures and options traded on the derivatives market of the Moscow stock exchange. The phenomenon of volatility clustering is not quite studied on the Russian stock exchange, and is not widely considered in scientific research papers which is stating the urgency of the problem. The aim is to identify and study the phenomenon of volatility clustering in the Russian market. To achieve this goal there have been performed the following tasks: identification of the nature of volatility clustering and its behavior in the Moscow stock exchange, the definition of the level of volatility clustering in the futures market, the modeling of volatility using GARCH models and evaluation of the quality of the results. Detailed research has shown that the nonlinear and asymmetric GARCH model best fits the behavior of the conditional variance. Further quantitative analysis showed that applying the conditional volatility of the model NAGARCH to option pricing Black-Scholes model reduces the standard deviation between the estimated price and established price in the futures market, compared with the of volatility in terms of the common determination.

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